Construction and Application of YOLOv3-Based Diatom Identification Model of Scanning Electron Microscope Images.
10.12116/j.issn.1004-5619.2021.410903
- Author:
Ji CHEN
1
;
Xiao-Rong LIU
2
;
Jia-Wen YANG
1
;
Ye-Qiu CHEN
2
;
Cheng WANG
2
;
Meng-Yuan OU
2
;
Jia-Yi WU
1
;
You-Jia YU
1
;
Kai LI
1
;
Peng CHEN
1
;
Feng CHEN
1
Author Information
1. Department of Forensic Medicine, Nanjing Medical University, Nanjing 211126, China.
2. Department of Water Public Security, Nanjing Public Security Bureau, Nanjing 210036, China.
- Publication Type:Journal Article
- Keywords:
YOLOv3;
artificial intelligence;
deep learning;
diatom test;
forensic pathology;
image recognition;
scanning electron microscope
- MeSH:
Diatoms;
Drowning/diagnosis*;
Humans;
Liver/diagnostic imaging*;
Lung/diagnostic imaging*;
Microscopy, Electron, Scanning
- From:
Journal of Forensic Medicine
2022;38(1):46-52
- CountryChina
- Language:English
-
Abstract:
OBJECTIVES:To construct a YOLOv3-based model for diatom identification in scanning electron microscope images, explore the application performance in practical cases and discuss the advantages of this model.
METHODS:A total of 25 000 scanning electron microscopy images were collected at 1 500× as an initial image set, and input into the YOLOv3 network to train the identification model after experts' annotation and image processing. Diatom scanning electron microscopy images of lung, liver and kidney tissues taken from 8 drowning cases were identified by this model under the threshold of 0.4, 0.6 and 0.8 respectively, and were also identified by experts manually. The application performance of this model was evaluated through the recognition speed, recall rate and precision rate.
RESULTS:The mean average precision of the model in the validation set and test set was 94.8% and 94.3%, respectively, and the average recall rate was 81.2% and 81.5%, respectively. The recognition speed of the model is more than 9 times faster than that of manual recognition. Under the threshold of 0.4, the mean recall rate and precision rate of diatoms in lung tissues were 89.6% and 87.8%, respectively. The overall recall rate in liver and kidney tissues was 100% and the precision rate was less than 5%. As the threshold increased, the recall rate in all tissues decreased and the precision rate increased. The F1 score of the model in lung tissues decreased with the increase of threshold, while the F1 score in liver and kidney tissues with the increase of threshold.
CONCLUSIONS:The YOLOv3-based diatom electron microscope images automatic identification model works at a rapid speed and shows high recall rates in all tissues and high precision rates in lung tissues under an appropriate threshold. The identification model greatly reduces the workload of manual recognition, and has a good application prospect.
